🤖 FTC/FRC Robotics
Design Engineering Process
1. Define the Problem
Identified the competitive robotics challenge: build robots that can autonomously and manually complete complex game objectives within strict time limits and rules.
2. Research & Gather Information
Studied game manuals, analyzed successful robot designs from previous seasons, researched mechanical components, sensors, and control systems available for FTC and FRC competitions.
3. Brainstorm Solutions
Generated multiple robot configurations including different drive trains (tank, mecanum, swerve), manipulator designs, and autonomous programming strategies for various game challenges.
4. Prototype & Build
Constructed initial robot prototypes using CAD software for design visualization, then fabricated physical models with 3D printed and machined components, motors, and sensors.
5. Test & Evaluate
Conducted iterative testing on practice fields, measuring performance metrics like speed, accuracy, reliability, and scoring efficiency during both autonomous and driver-controlled periods.
6. Refine & Improve
Analyzed test data to identify weaknesses, then iterated on designs with improved mechanisms, better sensor integration, optimized programming, and enhanced driver training.
Technical Implementation
Mechanical Design
- • CAD modeling for drivetrain and manipulator systems
- • Precision fabrication using CNC and 3D printing
- • Weight optimization and structural integrity analysis
Control Systems
- • Sensor integration (encoders, IMU, vision systems)
- • PID control algorithms for precise movement
- • Real-time telemetry and diagnostics
FRC Programming
- • Limelight AprilTag Vision Tracking: Integrated Limelight camera system with AprilTag detection for autonomous alignment and precise targeting of game elements
- • Swerve Drive with YAGSL: Implemented advanced swerve drive kinematics using the Yet Another Gyro Swerve Library (YAGSL) for omni-directional robot control and rotation
- • Command-Based Architecture: Built robot software using WPILib's command-based paradigm, organizing subsystems, commands, and trigger-based autonomous routines
- • Autonomous Sequences: Programmed complex autonomous paths using PathPlanner for smooth trajectories and integrated vision-based feedback
Competition Results
Successfully competed in multiple FTC and FRC regional tournaments, achieving consistent top rankings through systematic design iteration and team collaboration.
Photo Gallery
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